{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0b3f9a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "select dwd_prj_count_line.*,ods_prj_tgprjlist.project_year,ods_prj_tgprjlist.project_addr addr,\n",
      "IFNULL(bus_web_city.id,430000) city_id,voltage_classes\n",
      ",CASE dwd_prj_count_line.project_type WHEN  '陆上电缆线路' THEN 1 WHEN  '架空线路' THEN 2 else 3 end t\n",
      ",CASE voltage_classes WHEN  '交流110kV' THEN 110 WHEN  '交流220kV' THEN 220 WHEN  '交流35kV' THEN 350 else 550 end classes\n",
      "from dwd_prj_count_line left join ods_prj_tgprjlist on ods_prj_tgprjlist.project_id =\n",
      "dwd_prj_count_line.project_id left join bus_web_city on bus_web_city.name \n",
      "= concat(ods_prj_tgprjlist.project_addr,'市')\n",
      "-- where  bus_web_city.id ='430100'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import pymysql\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "sql_query = '''select dwd_prj_count_line.*,ods_prj_tgprjlist.project_year,ods_prj_tgprjlist.project_addr addr,\n",
    "IFNULL(bus_web_city.id,430000) city_id,voltage_classes\n",
    ",CASE dwd_prj_count_line.project_type WHEN  '陆上电缆线路' THEN 1 WHEN  '架空线路' THEN 2 else 3 end t\n",
    ",CASE voltage_classes WHEN  '交流110kV' THEN 110 WHEN  '交流220kV' THEN 220 WHEN  '交流35kV' THEN 350 else 550 end classes\n",
    "from dwd_prj_count_line left join ods_prj_tgprjlist on ods_prj_tgprjlist.project_id =\n",
    "dwd_prj_count_line.project_id left join bus_web_city on bus_web_city.name \n",
    "= concat(ods_prj_tgprjlist.project_addr,'市')\n",
    "-- where  bus_web_city.id ='430100'\n",
    "''' \n",
    "print(sql_query)\n",
    "engine = create_engine('mysql+pymysql://root:123456@localhost:3306/buildcost')\n",
    "data = pd.read_sql_query(sql_query, engine)\n",
    "# print(data.iloc[0])\n",
    "# data.columns = data.iloc[0]\n",
    "# print(data.info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c9d4d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                        id              project_name  \\\n",
      "16    03690fc6-c9df-11ea-81bc-d8c4973d59ec          松木桥牵~护城220kV线路工程   \n",
      "19    03692325-c9df-11ea-81bc-d8c4973d59ec      35kV西北太枨线π进葛家变送电线路工程   \n",
      "27    0369345e-c9df-11ea-81bc-d8c4973d59ec      长沙生药—集里π入浏阳220kV线路工程   \n",
      "28    03693506-c9df-11ea-81bc-d8c4973d59ec           永和—达浒变110kV线路工程   \n",
      "31    03693e7a-c9df-11ea-81bc-d8c4973d59ec          松木桥牵～墨山220kV线路工程   \n",
      "...                                    ...                       ...   \n",
      "1694  68b9f574-1c4e-11eb-8cb5-005056c00001                  配套光缆通信工程   \n",
      "1697  a9ef4fea-1a67-11eb-8f46-005056c00001  蛇形山-太平寺35kV 线路配套光纤通信光缆工程   \n",
      "1706  036d9548-c9df-11ea-81bc-d8c4973d59ec  望城110千伏变电站35千伏送出工程（架空部分）   \n",
      "1707  5b090047-1c1b-11eb-b796-005056c00001         艾家冲-楚沩Ⅱ回220kV线路工程   \n",
      "1708  6de6d469-1c4e-11eb-8cb5-005056c00001         艾家冲-楚沩Ⅱ回220kV线路工程   \n",
      "\n",
      "                                project_id      project_addr project_type  \\\n",
      "16    0e169eea-513c-4885-9a86-b84428e04334      国网湖南省电力公司建设部         架空线路   \n",
      "19    12cce79b-9915-47e9-8666-1422b046b962  国网湖南省电力公司长沙供电分公司         架空线路   \n",
      "27    1670f395-a5d2-4487-863b-732ab8040484      国网湖南省电力公司建设部         架空线路   \n",
      "28    1681fde8-2fe4-4acf-8f60-3b47b79b92a1  国网湖南省电力公司长沙供电分公司         架空线路   \n",
      "31    18a0c317-209c-4b55-81ed-6b338bf4e363      国网湖南省电力公司建设部         架空线路   \n",
      "...                                    ...               ...          ...   \n",
      "1694  b962eedc-9236-4ca7-9288-9b6e84337cfe  国网湖南省电力公司娄底供电分公司         通信线路   \n",
      "1697  b039181b-8316-4b6a-b0c4-81411e09a26b  国网湖南省电力公司娄底供电分公司         通信线路   \n",
      "1706  fe3aadd7-d37b-4c44-a827-c88378140d91  国网湖南省电力公司湘西供电分公司         架空线路   \n",
      "1707  e67fc737-12bc-4a5b-bf1d-eb6706306def    国网湖南省电力公司建设分公司         架空线路   \n",
      "1708  e67fc737-12bc-4a5b-bf1d-eb6706306def    国网湖南省电力公司建设分公司         架空线路   \n",
      "\n",
      "     ky_btgcf ky_qtfy ky_qzfy ky_jthj ky_dthj  ... project_year  addr city_id  \\\n",
      "16       None    None    None    None    None  ...         2019    长沙  430100   \n",
      "19       None    None    None    None    None  ...         2018    长沙  430100   \n",
      "27       None    None    None    None    None  ...         2019    长沙  430100   \n",
      "28       None    None    None    None    None  ...         2019    长沙  430100   \n",
      "31       None    None    None    None    None  ...         2019    长沙  430100   \n",
      "...       ...     ...     ...     ...     ...  ...          ...   ...     ...   \n",
      "1694     None    None    None    None    None  ...         2018    娄底  431300   \n",
      "1697     None    None    None    None    None  ...         2019    娄底  431300   \n",
      "1706     None    None    None    None    None  ...         2020    湘西  430000   \n",
      "1707     None    None    None    None    None  ...         2020  None  430000   \n",
      "1708     None    None    None    None    None  ...         2020  None  430000   \n",
      "\n",
      "     voltage_classes  t classes        gy        yj   yj_qtfy   yj_jthj  \n",
      "16           交流220kV  2     220 -0.763468 -0.026758  0.028153  0.029401  \n",
      "19            交流35kV  2     350 -0.563346 -0.029833  0.127036  0.057642  \n",
      "27           交流220kV  2     220 -0.768259 -0.012535  0.020369  0.027166  \n",
      "28           交流110kV  2     110 -0.702991 -0.066173  0.021407  0.029656  \n",
      "31           交流220kV  2     220 -0.788155 -0.001183  0.025461  0.049054  \n",
      "...              ... ..     ...       ...       ...       ...       ...  \n",
      "1694         交流110kV  3     110 -0.835531 -0.012202  0.000962  0.020128  \n",
      "1697          交流35kV  3     350 -0.814392 -0.007029  0.038710  0.039656  \n",
      "1706          交流35kV  2     350 -0.443742 -0.025581  0.002999  0.021370  \n",
      "1707         交流220kV  2     220 -0.761799 -0.033855 -0.081825 -0.003605  \n",
      "1708         交流220kV  2     220 -0.761799 -0.033855 -0.081825 -0.003605  \n",
      "\n",
      "[430 rows x 48 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-52b12b4c0f3e>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['gy'] = (data1['gs_qtfy'].astype(float)-data1['ys_btgcf'].astype(float))/data1['ys_btgcf'].astype(float)\n",
      "<ipython-input-17-52b12b4c0f3e>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['yj'] = (data1['ys_btgcf'].astype(float)-data1['js_btgcf'].astype(float))/data1['js_btgcf'].astype(float)\n",
      "<ipython-input-17-52b12b4c0f3e>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['yj_qtfy'] = (data1['ys_qtfy'].astype(float)-data1['js_qtfy'].astype(float))/data1['js_qtfy'].astype(float)\n",
      "<ipython-input-17-52b12b4c0f3e>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['yj_jthj'] = (data1['ys_jthj'].astype(float)-data1['js_jthj'].astype(float))/data1['js_jthj'].astype(float)\n"
     ]
    }
   ],
   "source": [
    "#可以通过subset参数来删除在gg和add中含有空数据的全部行\n",
    "data1 = data.dropna(subset=['city_id', 't', 'ys_btgcf','js_btgcf','project_year','voltage_classes'])\n",
    "data1['gy'] = (data1['gs_qtfy'].astype(float)-data1['ys_btgcf'].astype(float))/data1['ys_btgcf'].astype(float)\n",
    "data1['yj'] = (data1['ys_btgcf'].astype(float)-data1['js_btgcf'].astype(float))/data1['js_btgcf'].astype(float)\n",
    "data1['yj_qtfy'] = (data1['ys_qtfy'].astype(float)-data1['js_qtfy'].astype(float))/data1['js_qtfy'].astype(float)\n",
    "data1['yj_jthj'] = (data1['ys_jthj'].astype(float)-data1['js_jthj'].astype(float))/data1['js_jthj'].astype(float)\n",
    "# data1['yj_qtfy'] = (data1['ys_qtfy'].astype(float)-data1['js_qtfy'].astype(float))/data1['js_qtfy'].astype(float)\n",
    "x = sm.add_constant(data1[['city_id','project_year','t']]) #生成自变量\n",
    "# print(x)\n",
    "# # x= data1['project_addr'] \n",
    "y = data1['yj'] #生成因变量\n",
    "# # print(np.asarray(y))\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述\n",
    "print(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "be4c90c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2bbc5f9b7f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\py8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\py8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 中文支持\n",
    "plt.rcParams['savefig.dpi'] = 600 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 300 #分辨率\n",
    "plt.scatter(data1['city_id']*1.00003, data1['yj'], marker='.',c='r')\n",
    "plt.scatter(data1['city_id']*1.00006, data1['yj_qtfy'], marker='.',c='b')\n",
    "plt.scatter(data1['city_id']*1.00009, data1['yj_jthj'], marker='.',c='y')\n",
    "# plt.scatter(data1['city_id'], data1['yj_qtfy'], marker='.',c='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b8a99839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2bbc59a2ee0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data1['project_year'].astype(float)*1.00003, data1['yj'], marker='.',c='r')\n",
    "\n",
    "plt.scatter(data1['project_year'].astype(float)*1.00006, data1['yj_qtfy'], marker='.',c='b')\n",
    "plt.scatter(data1['project_year'].astype(float)*1.00009, data1['yj_jthj'], marker='.',c='y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6ce4db14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2bbc5fd1eb0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\py8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\py8\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(data1['project_type'], data1['yj'], marker='.')\n",
    "# plt.scatter(data1['voltage_classes'], data1['yj_sbgzf'], marker='.',c='b')\n",
    "plt.scatter(data1['classes']+2, data1['yj_qtfy'], marker='.',c='y')\n",
    "plt.scatter(data1['classes']+5, data1['yj_jthj'], marker='.',c='g')\n",
    "plt.scatter(data1['classes']+8, data1['yj'], marker='.',c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e13bb6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df    sum_sq   mean_sq          F        PR(>F)\n",
      "classes         1.0  0.025157  0.025157   6.420337  1.163950e-02\n",
      "city_id         1.0  0.042728  0.042728  10.904603  1.040109e-03\n",
      "project_year    1.0  0.181557  0.181557  46.334521  3.398892e-11\n",
      "Residual      426.0  1.669233  0.003918        NaN           NaN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-1eb730158240>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['project_year']=data1['project_year'].astype(float)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>yj</td>        <th>  R-squared:         </th> <td>   0.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   21.22</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th> <td>7.90e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:37:50</td>     <th>  Log-Likelihood:    </th> <td>  583.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   430</td>      <th>  AIC:               </th> <td>  -1159.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   426</td>      <th>  BIC:               </th> <td>  -1143.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>   79.7511</td> <td>   13.450</td> <td>    5.929</td> <td> 0.000</td> <td>   53.314</td> <td>  106.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td> 7.795e-06</td> <td> 7.08e-06</td> <td>    1.100</td> <td> 0.272</td> <td>-6.13e-06</td> <td> 2.17e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>   -0.0412</td> <td>    0.006</td> <td>   -6.807</td> <td> 0.000</td> <td>   -0.053</td> <td>   -0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>classes</th>      <td>-4.933e-05</td> <td> 2.51e-05</td> <td>   -1.965</td> <td> 0.050</td> <td>-9.87e-05</td> <td> 1.25e-08</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>412.380</td> <th>  Durbin-Watson:     </th> <td>   1.930</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>32779.274</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-3.757</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>45.108</td>  <th>  Cond. No.          </th> <td>1.92e+09</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.92e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     yj   R-squared:                       0.130\n",
       "Model:                            OLS   Adj. R-squared:                  0.124\n",
       "Method:                 Least Squares   F-statistic:                     21.22\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):           7.90e-13\n",
       "Time:                        16:37:50   Log-Likelihood:                 583.41\n",
       "No. Observations:                 430   AIC:                            -1159.\n",
       "Df Residuals:                     426   BIC:                            -1143.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const           79.7511     13.450      5.929      0.000      53.314     106.189\n",
       "city_id       7.795e-06   7.08e-06      1.100      0.272   -6.13e-06    2.17e-05\n",
       "project_year    -0.0412      0.006     -6.807      0.000      -0.053      -0.029\n",
       "classes      -4.933e-05   2.51e-05     -1.965      0.050   -9.87e-05    1.25e-08\n",
       "==============================================================================\n",
       "Omnibus:                      412.380   Durbin-Watson:                   1.930\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32779.274\n",
       "Skew:                          -3.757   Prob(JB):                         0.00\n",
       "Kurtosis:                      45.108   Cond. No.                     1.92e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.92e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "data1['project_year']=data1['project_year'].astype(float)\n",
    "formula = 'yj~ classes + city_id + project_year'\n",
    "anova_results = anova_lm(ols(formula,data1).fit())\n",
    "print(anova_results)# 本体工程\n",
    "\n",
    "x = sm.add_constant(data1[['city_id','project_year','classes']]) #生成自变量\n",
    "y = data1['yj'] #生成因变量\n",
    "# # print(np.asarray(y))\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e55edd07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df    sum_sq   mean_sq          F    PR(>F)\n",
      "classes         1.0  0.223009  0.223009  11.485773  0.000767\n",
      "city_id         1.0  0.075462  0.075462   3.886541  0.049321\n",
      "project_year    1.0  0.008361  0.008361   0.430608  0.512045\n",
      "Residual      426.0  8.271273  0.019416        NaN       NaN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>yj_qtfy</td>     <th>  R-squared:         </th> <td>   0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td>0.00141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:38:06</td>     <th>  Log-Likelihood:    </th> <td>  239.32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   430</td>      <th>  AIC:               </th> <td>  -470.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   426</td>      <th>  BIC:               </th> <td>  -454.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>  -31.9447</td> <td>   29.941</td> <td>   -1.067</td> <td> 0.287</td> <td>  -90.795</td> <td>   26.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td> 3.276e-05</td> <td> 1.58e-05</td> <td>    2.078</td> <td> 0.038</td> <td> 1.77e-06</td> <td> 6.38e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>    0.0088</td> <td>    0.013</td> <td>    0.656</td> <td> 0.512</td> <td>   -0.018</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>classes</th>      <td>    0.0002</td> <td> 5.59e-05</td> <td>    3.101</td> <td> 0.002</td> <td> 6.34e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>621.576</td> <th>  Durbin-Watson:     </th>  <td>   2.002</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>201517.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 7.210</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>108.069</td> <th>  Cond. No.          </th>  <td>1.92e+09</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.92e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                yj_qtfy   R-squared:                       0.036\n",
       "Model:                            OLS   Adj. R-squared:                  0.029\n",
       "Method:                 Least Squares   F-statistic:                     5.268\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):            0.00141\n",
       "Time:                        16:38:06   Log-Likelihood:                 239.32\n",
       "No. Observations:                 430   AIC:                            -470.6\n",
       "Df Residuals:                     426   BIC:                            -454.4\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const          -31.9447     29.941     -1.067      0.287     -90.795      26.906\n",
       "city_id       3.276e-05   1.58e-05      2.078      0.038    1.77e-06    6.38e-05\n",
       "project_year     0.0088      0.013      0.656      0.512      -0.018       0.035\n",
       "classes          0.0002   5.59e-05      3.101      0.002    6.34e-05       0.000\n",
       "==============================================================================\n",
       "Omnibus:                      621.576   Durbin-Watson:                   2.002\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           201517.255\n",
       "Skew:                           7.210   Prob(JB):                         0.00\n",
       "Kurtosis:                     108.069   Cond. No.                     1.92e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.92e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'yj_qtfy~ classes + city_id + project_year'\n",
    "anova_results = anova_lm(ols(formula,data1).fit())\n",
    "print(anova_results)# 其他工程\n",
    "y = data1['yj_qtfy'] #生成因变量\n",
    "# # print(np.asarray(y))\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca5b861f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df    sum_sq   mean_sq         F    PR(>F)\n",
      "classes         1.0  0.000065  0.000065  0.024822  0.874886\n",
      "city_id         1.0  0.013610  0.013610  5.158787  0.023627\n",
      "project_year    1.0  0.000156  0.000156  0.058964  0.808257\n",
      "Residual      426.0  1.123869  0.002638       NaN       NaN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>yj_jthj</td>     <th>  R-squared:         </th> <td>   0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 29 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.157</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:38:20</td>     <th>  Log-Likelihood:    </th> <td>  668.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   430</td>      <th>  AIC:               </th> <td>  -1329.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   426</td>      <th>  BIC:               </th> <td>  -1313.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>   -2.7702</td> <td>   11.037</td> <td>   -0.251</td> <td> 0.802</td> <td>  -24.463</td> <td>   18.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>city_id</th>      <td> 1.217e-05</td> <td> 5.81e-06</td> <td>    2.093</td> <td> 0.037</td> <td>  7.4e-07</td> <td> 2.36e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>project_year</th> <td>   -0.0012</td> <td>    0.005</td> <td>   -0.243</td> <td> 0.808</td> <td>   -0.011</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>classes</th>      <td>-2.344e-07</td> <td> 2.06e-05</td> <td>   -0.011</td> <td> 0.991</td> <td>-4.07e-05</td> <td> 4.03e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>762.621</td> <th>  Durbin-Watson:     </th>  <td>   1.973</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>512890.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-10.660</td> <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>170.845</td> <th>  Cond. No.          </th>  <td>1.92e+09</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.92e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                yj_jthj   R-squared:                       0.012\n",
       "Model:                            OLS   Adj. R-squared:                  0.005\n",
       "Method:                 Least Squares   F-statistic:                     1.748\n",
       "Date:                Thu, 29 Apr 2021   Prob (F-statistic):              0.157\n",
       "Time:                        16:38:20   Log-Likelihood:                 668.46\n",
       "No. Observations:                 430   AIC:                            -1329.\n",
       "Df Residuals:                     426   BIC:                            -1313.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const           -2.7702     11.037     -0.251      0.802     -24.463      18.923\n",
       "city_id       1.217e-05   5.81e-06      2.093      0.037     7.4e-07    2.36e-05\n",
       "project_year    -0.0012      0.005     -0.243      0.808      -0.011       0.009\n",
       "classes      -2.344e-07   2.06e-05     -0.011      0.991   -4.07e-05    4.03e-05\n",
       "==============================================================================\n",
       "Omnibus:                      762.621   Durbin-Watson:                   1.973\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           512890.351\n",
       "Skew:                         -10.660   Prob(JB):                         0.00\n",
       "Kurtosis:                     170.845   Cond. No.                     1.92e+09\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.92e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'yj_jthj~ classes + city_id + project_year'\n",
    "anova_results = anova_lm(ols(formula,data1).fit())\n",
    "print(anova_results)# 合计\n",
    "y = data1['yj_jthj'] #生成因变量\n",
    "# # print(np.asarray(y))\n",
    "model = sm.OLS(y, x.astype(float)) #生成模型\n",
    "result = model.fit() #模型拟合\n",
    "result.summary() #模型描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ba552e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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